Create vtoonify/model/encoder/encoders/model_irse.py

vtoonify/model/encoder/encoders/model_irse.py

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+
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
+from model.encoder.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
+
+"""
+Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
+"""
+
+
+class Backbone(Module):
+	def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
+		super(Backbone, self).__init__()
+		assert input_size in [112, 224], "input_size should be 112 or 224"
+		assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
+		assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
+		blocks = get_blocks(num_layers)
+		if mode == 'ir':
+			unit_module = bottleneck_IR
+		elif mode == 'ir_se':
+			unit_module = bottleneck_IR_SE
+		self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
+									  BatchNorm2d(64),
+									  PReLU(64))
+		if input_size == 112:
+			self.output_layer = Sequential(BatchNorm2d(512),
+			                               Dropout(drop_ratio),
+			                               Flatten(),
+			                               Linear(512 * 7 * 7, 512),
+			                               BatchNorm1d(512, affine=affine))
+		else:
+			self.output_layer = Sequential(BatchNorm2d(512),
+			                               Dropout(drop_ratio),
+			                               Flatten(),
+			                               Linear(512 * 14 * 14, 512),
+			                               BatchNorm1d(512, affine=affine))
+
+		modules = []
+		for block in blocks:
+			for bottleneck in block:
+				modules.append(unit_module(bottleneck.in_channel,
+										   bottleneck.depth,
+										   bottleneck.stride))
+		self.body = Sequential(*modules)
+
+	def forward(self, x):
+		x = self.input_layer(x)
+		x = self.body(x)
+		x = self.output_layer(x)
+		return l2_norm(x)
+
+
+def IR_50(input_size):
+	"""Constructs a ir-50 model."""
+	model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_101(input_size):
+	"""Constructs a ir-101 model."""
+	model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_152(input_size):
+	"""Constructs a ir-152 model."""
+	model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_SE_50(input_size):
+	"""Constructs a ir_se-50 model."""
+	model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_SE_101(input_size):
+	"""Constructs a ir_se-101 model."""
+	model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_SE_152(input_size):
+	"""Constructs a ir_se-152 model."""
+	model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
+	return model
+